The financial services industry is experiencing a swift adoption of artificial intelligence (AI) and machine learning for a variety of applications. These technologies can be employed by both public and private sector entities to ensure adherence to regulatory requirements, monitor activities, evaluate data accuracy, and identify instances of fraudulent behavior. The utilization of artificial intelligence (AI) and machine learning (ML) has the potential to provide novel and unforeseen manifestations of interconnectivity within financial markets and institutions. This can be represented by the adoption of previously disparate data sources by diverse institutions. The researchers employed convenience sampling as the sampling method. The form was filled out over the period spanning from July 2023 to February 2024, and it was designed to be both anonymous and accessible through online and offline platforms. To assess the reliability and validity of the measurement scales and evaluate the structural model, we employed Partial Least Squares (PLS) for model validation. Specifically, we have used the software package Smart-PLS 3 with a bootstrapping of 5000 samples to estimate the significance of the parameters. The results indicate a positive and direct connection between artificial intelligence (AI) and either financial services or financial institutions. On the contrary, machine learning (ML) exhibits a strong and positive association among financial services and financial institutions. Similarly, there exists a positive and direct connection between AI and investors, as well as between ML and investors.

This paper presents a quantitative exploration of the functionality of cost accounting systems and their determinants in social welfare organizations. We conducted a questionnaire survey of managers of social welfare organizations running special nursing homes for the elderly and conducted a cluster analysis based on the data collected. The questionnaire was created based on the scales used in previous studies, with some new scales developed. For data analysis, the statistical analysis environment R was used. The clValid package of R was used to assess the validity of the cluster analysis. Based on the results of the analysis in this paper, it is expected that social welfare organizations that pursue cost leadership strategies and have a strong public interest orientation will benefit greatly by being able to utilize a highly functional cost accounting system. Such organizations will be able to improve their business efficiency by utilizing cost information, and their social contribution activities based on the resulting resources will truly be a contribution to public welfare. The findings from this study are of practical significance because they can be used by business managers of social welfare organizations to review the functionality of their cost accounting systems. We also focus on the degree to which nonprofit organizations focus on social contribution activities (in this paper, we call this public interest orientation). The public interest orientation of an organization is thought to affect the functionality of the cost accounting system in the same way as the organization’s strategy, but there has not been enough quantitative research on this point. By focusing on the public interest orientation of social welfare organizations, this study contributes to deepening our knowledge in this area.

The construction of researcher profiles is crucial for modern research management and talent assessment. Given the decentralized nature of researcher information and evaluation challenges, we propose a profile system for Chinese researchers based on unsupervised machine learning and algorithms. This system builds comprehensive profiles based on researchers’ basic and behavior information dimensions. It employs Selenium and Web Crawler for real-time data retrieval from academic platforms, utilizes TF-IDF and BERT for expertise recognition, DTM for academic dynamics, and K-means clustering for profiling. The experimental results demonstrate that these methods are capable of more accurately mining the academic expertise of researchers and performing domain clustering scoring, thereby providing a scientific basis for the selection and academic evaluation of research talents. This interactive analysis system aims to provide an intuitive platform for profile construction and analysis.

Climate change is a pressing global challenge that requires immediate action. To address this issue effectively, it is essential to engage and empower the younger generation who will shape the future. This abstract presents the experience of Mohamed Bin Zayed University for Humanities (MBZUH) in UAE in promoting climate action through youth empowerment and environmental education.MBZUH has recognized the significance of incorporating environmental education into its curriculum to foster a generation of environmentally conscious individuals. Through a multidimensional approach, the university has developed innovative strategies to empower students, enabling them to become active participants in addressing climate change. These strategies encompass both formal and informal education, leveraging various platforms and partnerships to create a comprehensive learning environment.This study delves into the initiatives undertaken by MBZUH to empower youth in climate action. It explores the incorporation of environmental education across disciplines, integrating sustainability principles into existing courses, and offering specialized programs focused on environmental science and climate studies. Additionally, it highlights the university's efforts in promoting hands-on learning experiences, such as field trips, research projects, and community engagement, to deepen students' understanding of climate issues and inspire practical action.Furthermore, the study examines the role of MBZUH's collaboration with local and international organizations, governmental bodies, and the wider community in fostering youth empowerment and climate action. It showcases successful partnerships that have resulted in impactful initiatives, including awareness campaigns, capacity-building workshops, and youth-led environmental projects.By sharing the experience of MBZUH, this study aims to provide valuable insights and best practices for promoting climate action through youth empowerment and environmental education. It underscores the importance of empowering the next generation with the knowledge, skills, and motivation to become effective agents of change in addressing climate challenges.

This research investigates the determinants of digital transformation among Vietnamese logistics service providers (LSPs). Employing the Technological-Organizational-Environmental framework and Resource Fit theory, the study identifies key factors influencing this process across different three stages: digitization, digitalization, digital transformation. Data from in-depth interviews with industry experts and a survey of 390 LSPs were analyzed using covariance-based structural equation modeling (CB-SEM). The findings reveal that the factors influencing the digital transformation of Vietnamese LSPs evolve across different stages. In the initial phase, information technology infrastructure, financial resources, employee capabilities, external pressures, and support services are key determinants. As digitalization progresses, leadership emerges as a crucial factor alongside the existing ones. In the final stage, the impact of these factors persists, with leadership and employee capabilities becoming increasingly important.